Multi-criterion decision making-based multi-channel hierarchical fusion of digital breast tomosynthesis and digital mammography for breast mass discrimination
Multi-criterion decision making-based multi-channel hierarchical fusion of digital breast tomosynthesis and digital mammography for breast mass discrimination
复制标题
基于多准则决策的数字乳腺断层合成和数字乳腺X线摄影的多通道分层融合用于乳腺肿块判别
DOI：
10.1016/j.knosys.2021.107303
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发表时间：
2021
影响因子：
8.8
通讯作者：
Zhen Xin
中科院分区：
文献类型：
--
作者：
Wang Linjing;He Qiang;Wang Xuetao;Song Ting;Li Xin;Zhang Shuxu;Qin Genggeng;Chen Weiguo;Zhou Linghong;Zhen Xin
从乳房x线摄影图像中解码的多面特征可以描述乳腺肿块异质性的不同角度，在本研究中，我们旨在探索一种有效整合从数字乳房断层合成（DBT）和全视场数字乳房x线摄影（FFDM）中提取的多面肿块表征的方法，以增强乳腺癌的识别。基于深度卷积神经网络（DCNN）和多层感知器（MLP），提出了一种基于多准则决策的多通道融合（MDMF）框架，在决策层面融合多通道处理的不同乳腺肿块表征。提出了一种多模态图像和多通道融合的层次结构框架（HFMM），以整合DBT和FFDM的多模态信息。我们回顾性收集了441例DBT和FFDM患者，并提取了包括恶性、良性和正常组织的兴趣区域（roi）进行验证。MDMF对恶性、良性和正常肿块的受者工作特征曲线下面积（AUC）分别为93.14%、91.30%、97.35% （FFDM）和93.79%、95.16%、99.31% （DBT）。而HFMM进一步提高了恶性肿块、良性肿块和正常肿块的AUC，分别为94.14%、95.42%和99.56%。MDMF完成的FFDM和DBT的马修斯相关系数（MCC）分别为73.15%和81.02%，通过HFMM整合DBT和FFDM的多模态信息时，matthews相关系数（MCC）提高到81.72%。实验结果表明，与基准分类算法和融合架构相比，所提出的HFMM具有更好的判别性能，是乳腺癌筛查中乳腺肿块判别的实用工具。
Multifaceted features decoded from mammographic images may describe various perspectives of the breast mass heterogeneity, in this study, we aimed to explore a methodology to effectively integrate multifaceted mass representations extracted from the digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) to enhance breast cancer discrimination. A novel multi-criterion decision making-based multi-channel fusion (MDMF) framework was proposed to fuse different breast mass representations processed in multi-channels built on the deep convolutional neural network (DCNN) and the multilayer perceptron (MLP) at the decision level. A hierarchical framework (HFMM) was also developed for multi-modality images and multi-channel fusion to integrate multimodality information from DBT and FFDM. We retrospectively collected 441 patients with both DBT and FFDM, and the regions of interest (ROIs) covering the malignant, benign, and normal tissues were extracted for validation. The MDMF achieved the area under the receiver operating characteristic curve (AUC) of 93.14%, 91.30%, 97.35% (FFDM) and 93.79%, 95.16%, 99.31% (DBT) respectively for the malignant, benign and normal mass. While the HFMM further boosted the performance to AUC of malignant 94.14%, benign 95.42% and normal mass 99.56% The matthews correlation coefficient (MCC) were 73.15% and 81.02% for FFDM and DBT accomplished by MDMF, and enhanced to 81.72% when integrating the multimodality information from DBT and FFDM via the proposed HFMM. The experimental results suggested that the proposed HFMM achieved superior discriminative performance when compared with the benchmark classification algorithms and fusion architectures, rendering it a practical tool for breast mass discrimination in breast cancer screening.